Radiology is the branch of medical science dealing with medical imaging for the purpose of diagnosis and treatment. The practice of radiology often involves the usage of X-ray machines or other radiation devices to perform the diagnosis or administer the treatment. Other practices of radiology employ techniques that do not involve radiation, such as magnetic resonance imaging (MRI) and ultrasound. As a medical field, radiology can refer to two sub-fields, diagnostic radiology and therapeutic radiology.
Diagnostic radiology deals with the use of various imaging modalities to aid in the diagnosis of a disease or condition in a subject. Typically, a wide beam of X-rays at a relatively low dosage is generated from a radiation source and directed towards an imaging target. An imager positioned on the opposite side of the source with respect to the imaging target receives the incident radiation and an image is generated based on the received radiation. Newer technology and advanced techniques allow for improved image collection with the application of computerized tomography (CT) to medical imaging techniques. Conventional medical imaging processes involving CT scans typically produce a series of 2-dimensional images of a target area which can be subsequently combined using computerized algorithms to generate a 3-dimensional image or model of the target area.
Therapeutic radiology or radiation oncology involves the use of radiation to treat diseases such as cancer through the directed application of radiation to targeted areas. In radiation therapy, radiation is applied (typically as a beam) to one or more regions of the targeted area at pre-specified dosages. Since the radiation can be potentially harmful, extensive treatment planning may be conducted, sometimes far in advance of the actual treatment sessions, to pinpoint the exact location(s) to apply the beam, and to limit unnecessary exposure to the radiation to other areas in the subject. The treatment planning phase may include the performance of CT scanning or other medical imaging techniques to acquire image data that can be subsequently used to precisely calculate the proper position and orientation of the subject, location of one or more target areas within the subject, and to predict the dosage(s) of the radiation to be applied during therapy.
Traditionally, radiotherapy treatment plans are created by a human operator by manually defining optimization objectives to achieve a clinically acceptable plan. Recently, human operators can produce treatment plans automatically by utilizing existing clinical knowledge that is captured by an algorithm by using a training phase that requires the human operator to select examples for the algorithm.
Automatic planning by existing systems relies on the human operator to train the algorithm, which requires that the human operator has access to existing radiotherapy treatment plans. However, this may be prohibitive for clinics that are only starting to establish radiotherapy treatment. The problem is exacerbated since all of the data used to train an algorithm has to be accessible by the human operator responsible for training the algorithm and requires establishing communication links between all participants and knowledge bases. For beginning clinics and practices where such information is not available, automatic planning may not be an option at all.
Furthermore, once created, a treatment plan is often further optimized based on a variety of factors, such as the treatment condition, the patient, and available resources. However, optimizing a treatment plan manually is time consuming as the optimization objectives are iteratively changed and the resulting dose distribution may be repeatedly re-evaluated until an optimal plan is achieved.
A critical component of treatment planning is predicting the dosage and dose distribution of the radiation to be applied to the patient. In knowledge based dose prediction, information from previously planned radiation treatments are used to gain knowledge of what is an achievable dose distribution in a new case without performing the actual planning. One approach to knowledge based dose prediction is to use a set of the previously planned cases to create a prediction model that could then be used (without needing to store all information related to this training set) to predict the dose for a new case.
Typically, a prediction model contains information that is necessary to predict the dose distribution achieved for a given patient geometry if planning is performed according to techniques, objectives and trade-offs described by the model. These predictions can be transformed into optimization objectives that when used in combination with an optimization algorithm, produce a complete radiotherapy treatment plan. However, accumulating a library of treatment plans that covers a representative portion of patient variety in a single clinic may be difficult or impossible for certain treatment techniques due to their rarity. Transmitting patient sensitive data between multiple participants may be difficult due to local regulations.
Each model typically has certain regions where the model's predictions are valid; however, if geometric parameters of the new case differ too much from the geometric parameters planned by the training set, the dose predictions may no longer be reliable. In some instances, a clinic may have several models to cover a large variety of different regions. Sample treatment plans and models may also be shared between clinics, thereby increasing the number of available models even more. However, sharing individual models between multiple clinics results in clinics having possibly tens or hundreds of different, but possibly overlapping models. This may make clinical use of shared models tedious and inefficient.